Big Data in Retail: Personalizing the Buying Experience with IDH

Palanivelu Balasubramanian (Bala), Business Development Manager, Big-Data, Intel.

Bala has more than 25 years of experience in the information management (IM) and analytics domain. Over the years as a consultant, he has excellent track record in influencing customers and architecting solutions for fortune 100 customers in the IM space (Big data, BI, data warehousing….). He has held leadership roles in various capacities supporting sales and delivery organizations. Prior to joining Intel, he was the Practice Principal (FSI) within the Information Management & Analytics division at HP.  He joined HP with the acquisition of Knightsbridge Solutions.

The demands of the digitally-connected customers are growing constantly.  Higher expectations of products and services, fragmented customer loyalty, market saturation on existing products, proliferation of new brands, heightened public concerns, regulatory mandates concerning health, wellness, sustainability, environment and product safety are some of the key challenges the retail and consumer industry has to deal with. To meet the demands of an educated customer, it’s important to prioritize the customer based on their buying pattern and needs, segment the customer with other like customers and personalize the customer’s experience. This means better understand the customer (preferences, behavior, and sentiment), provide personalized service, measure to refine the service.

Exploring the retailer’s data allows them to better understand how their consumer influence others' purchase decisions. In addition, it helps retailers avoid spamming their consumers, giving them the insight they need to send the right offer to a smaller segment of their customer database, which might actually be interested in the offer and/or influence their friends' shopping decisions, rather than blasting a generic offer to all of their customers. Overall, better understanding their data allows retailers to connect to their customers via their preferred channels and improve the consumer experience. Most of all, it allows retailers to make a profit so that they can remain in business.

With the massive influx of data from different channels and other sources (customer data, product data, demographic information, purchase history, all interaction data - call center, mobile, social, supply chain data, …..), organizations need to smartly invest in storing, analyzing, and get insights from the data. The key steps are to build a data repository which has the customer 360 profile, next is to build personalized service engine using rule based, statistics oriented predictive algorithms and recommendation systems, next is to effectively reach to the right customers with right messages through right channels and finally have methods to measure the whole process and effectiveness. Organizations need to align their people, process and technology framework to support these new demands.

The Intel(r) Distribution for Apache Hadoop* (IDH) provides the capabilities to store data in a distributed, cost effective way and also provide core data research capabilities like mining, profiling, searching, match-merging, building predictive models, and machine learning methods. Another key feature that IDH provides is the ability to perform encryption of data at both file and Hbase environment. This would be an added benefit for any retailer to store PCI compliance data.   Hadoop framework provides capabilities to ingest data (Sqoop, Flume), store data (HDFS, HBase), process data (MapReduce, Pig), and mine data (Mahout) which are key to enable the personalization process. Intel invests and continuously strengthens the IDH framework to meet enterprise needs.